With QoS constraint multicast routing problem is an NP

这是用prolog写的几个程序：程序１是一个旅行社关系系统，程序２是一个家族关系系统，程序３是一个成绩管理系统-This is prolog written several programs : a procedure is a travel agency relations systems, procedures 2 is a family relationship systems, procedures 3 is a performance management system

LMS-Newton自适应算法源码 反正结果刻与LMS算法相比较，显示了较好的性能。-LMS-Newton adaptive algorithm source code in any case engraved with the LMS algorithm results compared, showing a better performance.

labview编写的EMD分解程序，欢迎有研究的一起讨论。-PC EMD prepared by the decomposition process, welcomed the study together.

基于神经网络的电离层F2层临界频率预测方法 基于神经网络的电离层F2层临界频率预测方法-Based on neural network of ionospheric layer F2 critical frequency forecasting methods based on neural network of ionospheric layer F2 critical frequency forecasting methods

标准遗传算法程序，包含详细的问题描述，如果想和大家讨论的话。

PID控制已经广泛的用于工业生产，但是对于PID参数的获得一直是该控制的难点。本例子为对PID控制有兴趣的人提供一种参考，该PID控制采用遗传算法。-PID control has been widely used in industrial production, but the PID parameters for the control of access has been difficult. The examples of PID control for those who are interested in providing a reference, the PID control using genetic algorithm.

BP分类器，用与识别不同模式。-BP classifier, and used to identify different models.

基于matlab的改进模拟退火算法（经典）-Matlab-based improvements in simulated annealing algorithm (classical)

遗传算法的文章- Heredity algorithm article

10。《用于最优化的计算智能》，Nirwan Ansali，Edwin Hou着，李军，边肇棋译 清华大学出版社 1999年第一版 本书从讨论组合优化中的基本问题――NP问题入手，系统地讲述了近年来所发展起来的智能最优化的各种技术和方法，其中包括启发式搜索、Hopfield神经网络、模拟退火和随机机、均场退火以及遗传算法等；并在此基础上，通过一些典型的应用问题，如旅行商问题、模式识别中的点模式匹配问题、通信和任务调度等问题进一步阐明以上一些基本方法怎样用来解决这些原来具有NP性质的困难问题。本书是作者在美国新泽西州理工学院多年讲授有关课程的基础上写成的。全书深入浅出，理论联系实际。为帮助学生掌握基本概念，提高学习能动性，各章编写了习题。本书可作为通信、计算机、控制各专业的高年级学生和研究生学习有关课程的教材。它对于广大科研工作者也是一本很有实际价值的参考书。 -10. "Optimal for Computational Intelligence," Nirwan Ansali, with Edwin Hou, Li Jun, Pin Zhaoxing chess translation Tsinghua University Press in 1999 the first book version of combinatorial optimization from the discussion of basic questions-- NP problem start, the system described in recent years to develop the intelligent optimization of the technology and methods, These include heuristic search, Hopfield networks, simulated annealing and random machine, mean field annealing and genetic algorithms; and on this basis and through some typical applications, such as the traveling salesman probl